{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA Correlation Circle\n",
    "A function to provide a correlation circle for pca\n",
    "\n",
    "> from mlxtend.plotting import plot_pca_correlation_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In a so called correlation circle, the correlations between the original dataset features and the principal component(s) are shown via coordinates. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following correlation circle examples visualizes the correlation between the first two principal components and the 4 original iris dataset features.\n",
    "\n",
    "- Features with a positive correlation will be grouped together.\n",
    "- Totally uncorrelated features are orthogonal to each other.\n",
    "- Features with a negative correlation will be plotted on the opposing quadrants of this plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.data import iris_data\n",
    "from mlxtend.plotting import plot_pca_correlation_graph\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = iris_data()\n",
    "\n",
    "X_norm = X / X.std(axis=0) # Normalizing the feature columns is recommended\n",
    "\n",
    "feature_names = [\n",
    "  'sepal length',\n",
    "  'sepal width',\n",
    "  'petal length',\n",
    "  'petal width']\n",
    "\n",
    "figure, correlation_matrix = plot_pca_correlation_graph(X_norm, \n",
    "                                                        feature_names,\n",
    "                                                        dimensions=(1, 2),\n",
    "                                                        figure_axis_size=10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dim 1</th>\n",
       "      <th>Dim 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal length</th>\n",
       "      <td>-0.891224</td>\n",
       "      <td>-0.357352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal width</th>\n",
       "      <td>0.449313</td>\n",
       "      <td>-0.888351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal length</th>\n",
       "      <td>-0.991684</td>\n",
       "      <td>-0.020247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal width</th>\n",
       "      <td>-0.964996</td>\n",
       "      <td>-0.062786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Dim 1     Dim 2\n",
       "sepal length -0.891224 -0.357352\n",
       "sepal width   0.449313 -0.888351\n",
       "petal length -0.991684 -0.020247\n",
       "petal width  -0.964996 -0.062786"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Further, note that the percentage values shown on the x and y axis denote how much of the variance in the original dataset is explained by each principal component axis. I.e.., if PC1 lists 72.7% and PC2 lists 23.0% as shown above, then combined, the 2 principal components explain 95.7% of the total variance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## plot_pca_correlation_graph\n",
      "\n",
      "*plot_pca_correlation_graph(X, variables_names, dimensions=(1, 2), figure_axis_size=6, X_pca=None, explained_variance=None)*\n",
      "\n",
      "Compute the PCA for X and plots the Correlation graph\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : 2d array like.\n",
      "\n",
      "    The columns represent the different variables and the rows are the\n",
      "    samples of thos variables\n",
      "\n",
      "\n",
      "- `variables_names` : array like\n",
      "\n",
      "    Name of the columns (the variables) of X\n",
      "\n",
      "    dimensions: tuple with two elements.\n",
      "    dimensions to be plotted (x,y)\n",
      "\n",
      "    figure_axis_size :\n",
      "    size of the final frame. The figure created is a square with length\n",
      "    and width equal to figure_axis_size.\n",
      "\n",
      "\n",
      "- `X_pca` : np.ndarray, shape = [n_samples, n_components].\n",
      "\n",
      "    Optional.\n",
      "    `X_pca` is the matrix of the transformed components from X.\n",
      "    If not provided, the function computes PCA automatically using\n",
      "    mlxtend.feature_extraction.PrincipalComponentAnalysis\n",
      "    Expected `n_componentes >= max(dimensions)`\n",
      "\n",
      "\n",
      "- `explained_variance` : 1 dimension np.ndarray, length = n_components\n",
      "\n",
      "    Optional.\n",
      "    `explained_variance` are the eigenvalues from the diagonalized\n",
      "    covariance matrix on the PCA transformatiopn.\n",
      "    If not provided, the function computes PCA independently\n",
      "    Expected `n_componentes == X.shape[1]`\n",
      "\n",
      "**Returns**\n",
      "\n",
      "matplotlib_figure , correlation_matrix\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.plotting/plot_pca_correlation_graph.md', 'r') as f:\n",
    "    print(f.read())"
   ]
  }
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